'name': 'mask',
'value': Mask.RANDOM
},
# {'name': 'learning_rate', 'value': 0.01},
# (parameters for NECTR)
# {'name': 'nectr_n_hidden_layers', 'min': 1, 'max': 2, 'step': 1},
# {'name': 'nectr_n_neurons', 'min': 5, 'max': 45, 'step': 10}]
# {'name': 'nectr_poisson', 'value': True},
# {'name': 'nectr_item_counts', 'value': True},
# {'name': 'nectr_train_tf_on_solutions', 'value': False},
# {'name': 'nectr_learning_rate', 'value': 0.1},
# {'name': 'nectr_nn_regularization_type', 'value': 'l1'},
# {'name': 'nectr_nn_regularization', 'type': 'exp', 'min': 1e-2, 'max': 1e-2},
# {'name': 'nectr_lambda_completion', 'type': 'exp', 'min': 2e-2, 'max': 2e-0},
{
'name': 'nectr_n_epoch_completion',
'min': 2,
'max': 2,
'step': 2
}
]
# Setup DataHelper utils
DataHelper.setup(PATH_TO_DATA)
# Setup cross validation
cv = CrossValidation(datahelper=DataHelper, parameters=parameters)
# Run the cross validation pipeline
cv.run_pipeline(model, PATH_TO_RESULTS, plot=False)
